Functionally driven brain networks using multi-layer graph clustering.

We just began a large new study on
Autism, ADHD, Anxiety & Depression in 12-17 year-olds.
Read more and learn how you can help!

TitleFunctionally driven brain networks using multi-layer graph clustering.
Publication TypeJournal Article
Year of Publication2014
AuthorsGhanbari, Y, Bloy, L, Shankar, V, J Edgar, C, Roberts, TPL, Schultz, RT, Verma, R
JournalMed Image Comput Comput Assist Interv
IssuePt 3
Date Published2014
KeywordsAdolescent, Brain, Brain Mapping, Child, Child Development Disorders, Pervasive, Humans, Image Interpretation, Computer-Assisted, Magnetoencephalography, Male, Nerve Net, Reproducibility of Results, Sensitivity and Specificity

Connectivity analysis of resting state brain has provided a novel means of investigating brain networks in the study of neurodevelpmental disorders. The study of functional networks, often represented by high dimensional graphs, predicates on the ability of methods in succinctly extracting meaningful representative connectivity information at the subject and population level. This need motivates the development of techniques that can extract underlying network modules that characterize the connectivity in a population, while capturing variations of these modules at the individual level. In this paper, we propose a multi-layer raph clustering technique that fuses the information from a collection of connectivity networks of a population to extract the underlying common network modules that serve as network hubs for the population. These hubs form a functional network atlas. In addition, our technique provides subject-specific factors designed to characterize and quantify the degree of intra- and inter- connectivity between hubs, thereby providing a representation that is amenable to group level statistical analyses. We demonstrate the utility of the technique by creating a population network atlas of connectivity by examining MEG based functional connectivity in typically developing children, and using this to describe the individualized variation in those diagnosed with autism spectrum disorder.

Alternate JournalMed Image Comput Comput Assist Interv
PubMed ID25320789
PubMed Central IDPMC5331879
Grant ListR01 MH092862 / MH / NIMH NIH HHS / United States
R21 MH098010 / MH / NIMH NIH HHS / United States
RC1 MH088791 / MH / NIMH NIH HHS / United States
MH098010 / MH / NIMH NIH HHS / United States
Leave a Comment